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Unraveling the regulatory connections between two controllers of breast cancer cell fate

Estrogen receptor alpha (ERα) expression is critical for breast cancer classification, high ERα expression being associated with better prognosis. ERα levels strongly correlate with that of GATA binding protein 3 (GATA3), a major regulator of ERα expression. However, the mechanistic details of ERα–G...

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Autores principales: Lee, Jinho, Tiwari, Abhinav, Shum, Victor, Mills, Gordon B., Mancini, Michael A., Igoshin, Oleg A., Balázsi, Gábor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4066784/
https://www.ncbi.nlm.nih.gov/pubmed/24792166
http://dx.doi.org/10.1093/nar/gku360
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author Lee, Jinho
Tiwari, Abhinav
Shum, Victor
Mills, Gordon B.
Mancini, Michael A.
Igoshin, Oleg A.
Balázsi, Gábor
author_facet Lee, Jinho
Tiwari, Abhinav
Shum, Victor
Mills, Gordon B.
Mancini, Michael A.
Igoshin, Oleg A.
Balázsi, Gábor
author_sort Lee, Jinho
collection PubMed
description Estrogen receptor alpha (ERα) expression is critical for breast cancer classification, high ERα expression being associated with better prognosis. ERα levels strongly correlate with that of GATA binding protein 3 (GATA3), a major regulator of ERα expression. However, the mechanistic details of ERα–GATA3 regulation remain incompletely understood. Here we combine mathematical modeling with perturbation experiments to unravel the nature of regulatory connections in the ERα–GATA3 network. Through cell population-average, single-cell and single-nucleus measurements, we show that the cross-regulation between ERα and GATA3 amounts to overall negative feedback. Further, mathematical modeling reveals that GATA3 positively regulates its own expression and that ERα autoregulation is most likely absent. Lastly, we show that the two cross-regulatory connections in the ERα–GATA3 negative feedback network decrease the noise in ERα or GATA3 expression. This may ensure robust cell fate maintenance in the face of intracellular and environmental fluctuations, contributing to tissue homeostasis in normal conditions, but also to the maintenance of pathogenic states during cancer progression.
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spelling pubmed-40667842014-06-24 Unraveling the regulatory connections between two controllers of breast cancer cell fate Lee, Jinho Tiwari, Abhinav Shum, Victor Mills, Gordon B. Mancini, Michael A. Igoshin, Oleg A. Balázsi, Gábor Nucleic Acids Res Computational Biology Estrogen receptor alpha (ERα) expression is critical for breast cancer classification, high ERα expression being associated with better prognosis. ERα levels strongly correlate with that of GATA binding protein 3 (GATA3), a major regulator of ERα expression. However, the mechanistic details of ERα–GATA3 regulation remain incompletely understood. Here we combine mathematical modeling with perturbation experiments to unravel the nature of regulatory connections in the ERα–GATA3 network. Through cell population-average, single-cell and single-nucleus measurements, we show that the cross-regulation between ERα and GATA3 amounts to overall negative feedback. Further, mathematical modeling reveals that GATA3 positively regulates its own expression and that ERα autoregulation is most likely absent. Lastly, we show that the two cross-regulatory connections in the ERα–GATA3 negative feedback network decrease the noise in ERα or GATA3 expression. This may ensure robust cell fate maintenance in the face of intracellular and environmental fluctuations, contributing to tissue homeostasis in normal conditions, but also to the maintenance of pathogenic states during cancer progression. Oxford University Press 2014-07-01 2014-05-03 /pmc/articles/PMC4066784/ /pubmed/24792166 http://dx.doi.org/10.1093/nar/gku360 Text en © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Computational Biology
Lee, Jinho
Tiwari, Abhinav
Shum, Victor
Mills, Gordon B.
Mancini, Michael A.
Igoshin, Oleg A.
Balázsi, Gábor
Unraveling the regulatory connections between two controllers of breast cancer cell fate
title Unraveling the regulatory connections between two controllers of breast cancer cell fate
title_full Unraveling the regulatory connections between two controllers of breast cancer cell fate
title_fullStr Unraveling the regulatory connections between two controllers of breast cancer cell fate
title_full_unstemmed Unraveling the regulatory connections between two controllers of breast cancer cell fate
title_short Unraveling the regulatory connections between two controllers of breast cancer cell fate
title_sort unraveling the regulatory connections between two controllers of breast cancer cell fate
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4066784/
https://www.ncbi.nlm.nih.gov/pubmed/24792166
http://dx.doi.org/10.1093/nar/gku360
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